Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems. It works over many datatypes, with a preference for numpy arrays.

Added a new module: milk.ext.jugparallel to interface with jug
(http://luispedro.org/software/jug). This makes it easy to parallelise things
such as n-fold cross validation (each fold runs on its own processor) or
multiple kmeans random starts.

Add some new functions: measures.curves.precision_recall,
milk.unsupervised.kmeans.select_best.kmeans.